In all COVID-19 segmentation experiments, we use the same 2D U-Net architecture [1]} trained on the axial slices. We do not use a 3D model for two reasons. Firstly, as we show in Sec. REF , the images have a large difference in the inter-slice distances (from \(0.6\)  to \(8.0\)  mm), which can affect the performance of the 3D model. Secondly, authors of [2]} have shown 2D and 3D models yielding similar results in the same setup with various inter-slice distances. Moreover, we note that all considered methods are independent of the architecture choice. We also introduce the standard architectural modifications, replacing every convolution layer with the Residual Block [3]}. To train the segmentation model, we use binary cross-entropy (BCE) loss. Other training details are given in Sec. REF .
